How evidence is treated in modern macroeconomics

New Keynesian’ macroeconomist Simon Wren-Lewis has a post on his blog discussing how evidence is treated in modern macroeconomics (emphasis added):

It is hard to get academic macroeconomists trained since the 1980s to address this question, because they have been taught that these models and techniques are fatally flawed because of the Lucas critique and identification problems. But DSGE models as a guide for policy are also fatally flawed because they are too simple. The unique property that DSGE models have is internal consistency. Take a DSGE model, and alter a few equations so that they fit the data much better, and you have what could be called a structural econometric model. It is internally inconsistent, but because it fits the data better it may be a better guide for policy.

Being able to model a credible world, a world that somehow could be considered real or similar to the real world, is not the same as investigating the real world. Even though all theories are false, since they simplify, they may still possibly serve our pursuit of truth. But then they cannot be unrealistic or false in any way. The falsehood or unrealisticness has to be qualified (in terms of resemblance, relevance, etc.). At the very least, the minimalist demand on models in terms of credibility has to give away to a stronger epistemic demand of appropriate similarity and plausibility. One could of course also ask for a sensitivity or robustness analysis, but the credible world, even after having tested it for sensitivity and robustness, can still be a far way from reality – and unfortunately often in ways we know are important. Robustness of claims in a model does not per se give a warrant for exporting the claims to real world target systems.

Questions of external validity are important more specifically also when it comes to microfounded DSGE macromodels. It can never be enough that these models somehow are regarded as internally consistent. One always also has to pose questions of consistency with the data. Internal consistency without external validity is worth nothing.

Yours truly and people like Tony Lawson have for many years been urging economists to pay attention to the ontological foundations of their assumptions and models. Sad to say, economists have not paid much attention — and so modern economics has become increasingly irrelevant to the understanding of the real world.

Within mainstream economics internal validity is still everything and external validity nothing. Why anyone should be interested in that kind of theories and models is beyond imagination. As long as mainstream economists do not come up with any export-licenses for their theories and models to the real world in which we live, they really should not be surprised if people say that this is not science, but autism!

Since fully-fledged experiments on a societal scale as a rule are prohibitively expensive, ethically indefensible or unmanageable, economic theorists have to substitute experimenting with something else. To understand and explain relations between different entities in the real economy the predominant strategy is to build models and make things happen in these “analogue-economy models” rather than engineering things happening in real economies.

Formalistic deductive “Glasperlenspiel” can be very impressive and seductive. But in the realm of science it ought to be considered of little or no value to simply make claims about the model and lose sight of reality.

Neoclassical economics has since long given up on the real world and contents itself with proving things about thought up worlds. Empirical evidence only plays a minor role in economic theory, where models largely function as a substitute for empirical evidence. Hopefully humbled by the manifest failure of its theoretical pretences, the one-sided, almost religious, insistence on axiomatic-deductivist modeling as the only scientific activity worthy of pursuing in economics will give way to methodological pluralism based on ontological considerations rather than formalistic tractability.

To have valid evidence is not enough. What economics needs is sound evidence. Why? Simply because the premises of a valid argument do not have to be true, but a sound argument, on the other hand, is not only valid, but builds on premises that are true. Aiming only for validity, without soundness, is setting the economics aspirations level too low for developing a realist and relevant science.

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Weather prediction another area where you have to model a chaotic large-scale system and you can’t make experiments. Yet meteorologists manage to make useful predictions. One difference is weather models don’t look like weather. The elements in a weather simulation are pockets of air with a certain pressure, humidity, velocity, etc. and it’s the large scale interaction of these elements that produces the weather prediction as an emergent phenomenon. By improving the data collection on initial conditions, as well as the subtlety of the micro-model and the scale and depth of the simulation, and then comparing their prediction to whether it rains or not, meteorologists continually improve their predictive power.

Where is that in economics? Who is doing economics the way physicists and meteorologists do physics, by simulating billions of individual agents? Where are the models where the sims are not ‘homo economicus’ but varied individuals where one is working through college, another moonlights to feed two kids, and a third one buys a car or supports a mistress as a mid-lifer? Who collects statistics to initialise and tune that model, and who’s writing the parallel code to do the population-scale simulation? Who’s running even single-fold simulations on their supercomputers? Who’s running several-fold simulations to test both robustness and fit to real world data. Do econ departments even have supercomputers? Why not?

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